Trending insights is a computational study of finding significant topics that best represent the insights in a text corpus. An effective approach to identifying trending insights may reveal: (1) trending discussions on specific topics in online forums, such as around a particular brand, product, or service; (2) early signals on what topics might go viral in social media; and (3) emerging sentiment and drivers. A challenge to identifying trending insights comes in the form of the tremendous amount of unstructured data in the form of text that is available online. The data originates from multiple channels, such as product reviews, market research, customer care conversations, and social media. While it is clear that text contains valuable information, it is often less clear on how to best analyze such data at scale. Another challenge is the complex nature of many written languages (including English), which makes it difficult to find the most important topics efficiently.
Some approaches to identifying trending insights are mostly statistical, ranging from simply counting the frequency of words to more advanced methods, such as Latent Dirichlet Allocation (LDA). However, these approaches suffer from a few key issues: (1) too much noise or false positives (e.g., too generic phrases such as “thanks” and “contact us” or too frequently appearing words such as “breaking news”) are generated; (2) a significant amount of duplication; (3) a significant computational cost; and (4) the results are not intuitive to interpret.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.